A Contrastive Framework for Modeling Brain Heterogeneity in Precision Mental Health.

Xiaoyu Tong, Kanhao Zhao, Feng Vankee Lin, Yu Zhang

Abstract readReview

In one paragraph

Review in Biological psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaoyu TongDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.

Kanhao ZhaoGoodman-Luskin Microbiome Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.

Feng Vankee LinDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Stanford Institute for Human-Centered AI (HAI), Stanford, CA, USA.

Yu ZhangDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA; Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA; Stanford Institute for Human-Centered AI (HAI), Stanford, CA, USA. Electronic address: yzhangsu@stanford.edu.

Funding

Establishing Multimodal Brain Biomarkers Using Data-driven Analyticsfor Treatment Selection in DepressionR01MH129694 · NIMH · STANFORD UNIVERSITY · PI Yu Zhang · 2023 to 2026

$2.8M

Assess Neural Circuits and Subtypes Underlying Dimensions of Neuropsychiatric Symptoms in Alzheimer's DiseaseR21AG080425 · NIA · LEHIGH UNIVERSITY · PI ZHANG, YU · 2023 to 2024

$426k

NIA NIH HHS R21 AG080425NIMH NIH HHS R01 MH129694

6 · The paper itself

Abstract

Precision mental health aims to enable personalized care for mental disorders via the identification of brain-behavior associations at the individual level, yet current frameworks often face challenges in generalizability, interpretability, and clinical translation. Contrastive machine learning (CML) has emerged as a promising paradigm for characterizing individual brain variations by extracting brain dimensions that capture both disease-relevant aberrations and inter-subject heterogeneity. In this Review, we synthesize the conceptual foundations and recent methodological advances of CML. We compare CML with related frameworks and illustrate how it integrates with subtyping and predictive modeling pipelines, situating it within the broader landscape of precision mental health. We then review emerging applications that use CML to link brain structure and function to cognition, emotion, and treatment response. Finally, we outline future directions of applications and methodological innovations where CML could further advance personalized diagnosis and intervention in mental health.

Indexed as

BiomarkersBrain connectivityContrastive machine learningNeuroimagingPrecision mental healthTreatment response

Identifiers

PMID42556688

DOI10.1016/j.biopsych.2026.07.022

PMCPMC13477595

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.